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#! /bin/bash

set -o errexit
set -o nounset
set -o xtrace

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list
sudo apt update

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# Start SSH agent
eval $(ssh-agent)

# Add specific key
ssh-add ~/.ssh/butterfly.pem

# Add default key (if needed for GitHub, etc)
ssh-add

# SSH config entries

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import os
import time
import pathlib
import tarfile
import tempfile

import torch

PROJECT_PATH = pathlib.Path(os.environ.get("PROJECT_PATH", "."))

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"""Elasticsearch backend that enables fuzzy search to all plaintext queries."""
from wagtail.search.backends.elasticsearch6 import (Elasticsearch6SearchBackend,
                                                    Elasticsearch6SearchQueryCompiler)


class ElasticsearchQueryCompilerWithFuzziness(Elasticsearch6SearchQueryCompiler):
    """
    Copy of Elasticsearch6SearchQueryCompiler class with a modified default query.
    Adds the "fuzziness" parameter to all queries so that we can return inexact
    matches for misspellings, etc.

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worker_processes auto;
rtmp_auto_push on;
events {}
rtmp {
    server {
        listen 1935;
        listen [::]:1935 ipv6only=on;

        application live {
            live on;

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def add_child_page(parent_page, instance):
    """
    Mimic the behavior of the "add_child" from Django treebeard.

    We can't use `parent_page.add_child(instance=child_page)` inside of
    migrations because historical models don't have access to class
    methods. This replicates the behavior of `add_child` by calculating
    the treebeard path, path depth and url path.

    https://django-treebeard.readthedocs.io/en/latest/api.html#treebeard.models.Node.add_child

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# https://craftcms.stackexchange.com/questions/13584/devmode-phpdotenv-variable-ignored

'devMode' => filter_var(getenv('DEV_MODE') ?: false, FILTER_VALIDATE_BOOLEAN),

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#! /usr/bin/env bash

# Install the SciPy stack on Amazon Linux and prepare it for AWS Lambda

yum -y update
yum -y groupinstall "Development Tools"
yum -y install blas --enablerepo=epel
yum -y install lapack --enablerepo=epel
yum -y install atlas-sse3-devel --enablerepo=epel
yum -y install Cython --enablerepo=epel

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HSPD=20000	
LSPD=1000	
ACC=300	
DEC=300
EO=3	
WHILE 1 = 1
  X50000
  Y50000
  WAITX
  WAITY

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#! /usr/bin/env python3

import json
import boto3
import argparse
import time

lambda_client = boto3.client('lambda')

function_choices = [f['FunctionName'] for f in lambda_client.list_functions()['Functions']]